Advanced Topics in Software Engineering Marjan Sirjani Tehran University Faculty of Engineering ECE Department Tehran,

2 Subjects to be covered Modeling concurrency Formal verification methods Transition systems Petri Nets Process Algebra Actor Model Rebeca: an actor-based model Reo: a coordination language Constraint automata

3 Models of concurrency The Temporal Logic of Reactive and Concurrent Systems (Specification), Z. Manna, A. Pnueli, Springer-Verlag, 1992 Part one: Models of Concurrency Process algebra Communicating Sequential Processes C.A.R. Hoare, 2004

4 Actors Actors: a Unifying Model for Parallel and Distributed Computing, Agha G., Kim W., Open Systems Laboratory, Rebeca Modeling and Verification of Reactive Systems using Rebeca, Sirjani M., Movaghar A, Shali A., and de Boer F., Fundamenta Informaticae, Dec. 2004

5 Coordination languages Reo: A Channel-based Coordination Model for Component Composition, F. Arbab, Mathematical Structures in Computer Science, 2004 Modeling Component Connectors in Reo by Constraint Automata, F. Arbab, C. Baier, J.J.M.M. Rutten and M. Sirjani, in Proceedings of FOCLASA'03, Marseille, France, September 2003, ENTCS, Elsevier Science.

6 Overview Concurrent and Reactive Systems Formal methods Modeling language Process algebra, Petri nets, Actor languages Specification language Temporal logic, Automata Analysis Theorem proving, Model checking

7 Models of Concurrency Manna, chapter 1,2

8 Chapter 1- Basic Models Programs and systems they control Transformational Reactive

9 Transformational program More conventional Produce final result at the end of a terminating computation A function from an initial state to a final state Appropriately specified by properly characterizing the relation between initial and final states: predicate logic

10 Reactive program Not to produce a final result but to maintain some ongoing interaction with its environment

11 Reactivity and Concurrency Program and its environment act concurrently in transformational case, they act sequentially When we have parallel processes, even if the whole program has a transformational role, it should be analyzed as a reactive system.

12 Reactive systems Communication Coordination

13 Communication Shared variables Message passing Remote procedure calls

14 Coordination Semaphores Critical regions Monitors Handshaking Rendezvous Asynchronous transmission

15 The Generic Model V – Vocabulary E – Expressions A – Assertions I - Interpretations

16 V – Vocabulary A countable set of typed variables. Data variables Range over data domains used in programs, such as booleans, integers, or lists. Control variables Indicate progress in the execution of a program, range over locations in the program.

17 E – Expressions Expressions are constructed from the variables of V and constants (such as +,,  ) and predicates (such as >, null, and  ) over the appropriate domains (such as integers, lists, and sets) are applied. x+3y hd(u) tl(v) A  B

18 A – Assertions Assertions are constructed out of boolean expressions using boolean connectives and quantification( ,  ) over some variables that appear in the expressions.

19 I – Interpretation An interpretation I  I of a set of typed variables V  V is a mapping that assigns to each variable y  V a value I[y] in the domain of y. If I[  ]=T, we say I satisfies  : I |= 

20 Basic Transition System A basic transition system ( , , ,  ), intended to represent a reactive program.  ={u 1,…,u 2 }  V – a finite set of flexible state variables.  - a set of states.  - a finite set of transitions.  - an initial condition.

21  ={u 1,…,u 2 }  V – a finite set of flexible state variables. Data variables Explicitly declared and manipulated Control variables Represent progress in the execution of the program (label of a statement)

22  - a set of states. Each state s in  is an interpretation of , assigning to each variable u in  a value over its domain, denoted by s[u]. A state s that satisfies an assertion , i.e., s |= , is sometimes referred to as  –state.

23  - a finite set of transitions. Each transition  in T represents a state-transforming action of the system and is defined as a function  :   2  that maps a state s in  into the (possibly empty) set of states  (s) that can be obtained by applying action  to state s.

24  - an initial condition. This assertion characterizes the states at which execution of the program can begin. A state s that satisfies , i.e., s |= , is called an initial state.

25 The Transition Relation   Each transition  is characterized by an assertion, called the transition relation   ( ,  ’)   ( ,  ’): C  (  )  (y’ 1 =e 1 )  …  (y’ k =e k ) Enabling condition: C  (  ) Conjunction of modification statements

26 Enabled and disabled transitions Idling and diligent transitions Computation: infinite sequence of steps Computation prefix Reachable states

27 Concrete models Model 1: Transition Diagram Model 2: Shared-Variables text Model 3: Message-Passing text Model 4: Petri Nets

28 Model 1 : Transition diagrams Program P, and processes P i P::[declaration][P 1 || P 2 … ||P m ] m>=1 Data variables Y={y 1, …, y n } n>=1 Shared for all the processes

29 Declarations At the head of the program Modes, Types, Initial conditions mode var, …,var: type where  i Mode: in, local, out Types: basic (int,char), structured (array, list, set) Assertion  i, imposes constraint on the values of some of the variables in this statement

30 in k,n :integer where 0  k  n local y 1,y 2 : integer where y 1 =n  y 2 =1 out b : integer where b=1 Data precondition of the program   i  : 0  k  n  y 1 =n  y 2 =1  b=1

31 Processes Each process P i is represented by a transition diagram (directed graph) Nodes: locations For P i : L i ={l i 0, l i 1, …, l i ti } Entry and exit locations Edges: (atomic) instructions Guarded assignment c  [(y 1, …):=(e 1, …)] State of a program: Control variables (  i current location of control in P i )+ data variables

32 Diagrams as Basic Transition Systems State variables States Transition Initial condition

33 State variables All the data and control variables  = {  1, …,  m, y 1, …, y n } States All the possible interpretations that assign to the state variables values over their respective domains. Domain of control variable  I is the set of locations L i

34 Transition Idling transition  I is defined by transition relation  I : T Diligent transitions: labeled edges that appear within the processes.

35 l l’ C  [y i := e i ]   is the edge.   : (  i =l)  c  (  ’ i =l~)  (y i =e i )

36 Initial condition Program P: [dcl where  ][P 1 || … || P m ] Initial condition  :   /\ i=1 m (  I = l o i ) A process is enabled, or disabled on a state.

37 Example: Binomial coefficient ( n k ) = (n(n-1)…(n-k+1)) / (1.2….k)

38 Representing Concurrency by Interleaving X=0,Y=0 X:=1 Y:=1 X:=1 Y:=1 Program A Program B Process P1Process P2

39 Scheduling The choice of the enabled transition to be executed next. A sequence of choices that leads to a complete computation is called a schedule.

40 Model 2: shared-variable text